Distributed Data-Driven Iterative Learning Consensus Tracking for Nonlinear Discrete-Time Multiagent Systems

• 成果类型: Article
• 署名作者: Yu, Xian; Hou, Zhongsheng; Polycarpou, Marios M.
• 署名单位: The Chinese University of Hong Kong, Shenzhen; Qingdao University; University of Cyprus; University of Cyprus
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3105653
• 发表日期: 2022
• 页码: 3670-3677
• 关键词: Control systems; data models; trajectory; Heuristic algorithms; TOPOLOGY; Task analysis; nonlinear dynamical systems; Consensus tracking; data-driven iterative learning control; dynamic linearization technique; nonlinear repetitive discrete-time multiagent systems

摘要

In this article, a data-driven distributed leader-follower iterative learning consensus tracking control approach is proposed for unknown repetitive nonlinear nonaffine discrete-time multi-agent systems. The leader's command is only communicated to a subset of the following agents and each following agent exchanges information only with its neighbors under a directed graph. A local iterative learning consensus control protocol is designed using only local measurements communicated among neighboring agents without the availability of physical and structural information of each agent by virtue of the dynamic linearization method both on the agent and the ideal distributed learning controller along the iteration axis. The convergent consensus properties of the tracking errors along the iteration axis are rigorously established under the strongly connected iteration-independent and iteration-varying communication topologies. One example is provided to validate the effectiveness of the proposed iterative learning consensus control protocol.